[Mlir-commits] [mlir] [mlir][tosa] Lower MATMUL_T to Linalg (PR #222661)

[Jack Frankland]

================
@@ -95,6 +95,85 @@ func.func @matmul_dyn_output(%arg0: tensor<1x1x8xf32>, %arg1: tensor<1x8x1xf32>)
 
 // -----
 
+// CHECK-LABEL: @matmul_t
+func.func @matmul_t(%arg0: tensor<2x5x3xf32>, %arg1: tensor<2x6x3xf32>) -> tensor<2x5x6xf32> {
+  // CHECK: %[[C0:.+]] = arith.constant 0
+  // CHECK: %[[INIT:.+]] = tensor.empty()
+  // CHECK: %[[FILLED:.+]] = linalg.fill ins(%[[C0]] : f32) outs(%[[INIT]] : tensor<2x5x6xf32>) -> tensor<2x5x6xf32>
+  // CHECK: linalg.batch_matmul indexing_maps =
+  // CHECK-SAME: ins(%arg0, %arg1 : tensor<2x5x3xf32>, tensor<2x6x3xf32>) outs(%[[FILLED]] : tensor<2x5x6xf32>) -> tensor<2x5x6xf32>
+  %a_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
+  %b_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
+  %0 = tosa.matmul_t %arg0, %arg1, %a_zp, %b_zp : (tensor<2x5x3xf32>, tensor<2x6x3xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<2x5x6xf32>
+  return %0 : tensor<2x5x6xf32>
+}
+
+// -----
+
+// CHECK-LABEL: @matmul_t_broadcast
+func.func @matmul_t_broadcast(%arg0: tensor<4x5x3xf32>, %arg1: tensor<1x6x3xf32>) -> tensor<4x5x6xf32> {
+  // CHECK: %[[SLICE:.+]] = tensor.extract_slice %arg1[0, 0, 0] [1, 6, 3] [1, 1, 1] : tensor<1x6x3xf32> to tensor<6x3xf32>
+  // CHECK: %[[BCAST_INIT:.+]] = tensor.empty() : tensor<4x6x3xf32>
+  // CHECK: %[[BCAST:.+]] = linalg.broadcast ins(%[[SLICE]] : tensor<6x3xf32>) outs(%[[BCAST_INIT]] : tensor<4x6x3xf32>) dimensions = [0]
+  // CHECK: %[[C0:.+]] = arith.constant 0
+  // CHECK: %[[INIT:.+]] = tensor.empty()
+  // CHECK: %[[FILLED:.+]] = linalg.fill ins(%[[C0]] : f32) outs(%[[INIT]] : tensor<4x5x6xf32>) -> tensor<4x5x6xf32>
+  // CHECK: linalg.batch_matmul indexing_maps =
+  // CHECK-SAME: ins(%arg0, %[[BCAST]] : tensor<4x5x3xf32>, tensor<4x6x3xf32>) outs(%[[FILLED]] : tensor<4x5x6xf32>) -> tensor<4x5x6xf32>
+  %a_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
+  %b_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
+  %0 = tosa.matmul_t %arg0, %arg1, %a_zp, %b_zp : (tensor<4x5x3xf32>, tensor<1x6x3xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<4x5x6xf32>
+  return %0 : tensor<4x5x6xf32>
+}
+
----------------
FranklandJack wrote:

Yeah good idea, will add failure cases for these. Not sure if we should add negative tests since these will start failing once dialect support is added which I don't think should happen.

https://github.com/llvm/llvm-project/pull/222661